[{"title":"Markdown \u0026 Technical Showcase: Testing Every Feature","url":"https://adityabavadekar.github.io/blogs/markdown-showcase/","date":"2026-05-14","tags":["markdown","testing","showcase","tech"],"excerpt":"This post serves as a comprehensive showcase of all the styling and features supported by this Hugo setup. It includes everything from standard markdown to complex diagrams and custom shortcodes.\n1. Text Formatting # You can use bold text, italic text, or both. Need to strike something out? No problem. How about a link to the homepage?\nLists # Unordered # Item 1 Item 2 Nested Item 2a Nested Item 2b Item 3 Ordered # First major step Second major step Sub-step A Sub-step B Final conclusion 2. Technical Content # Code Blocks # Python with Syntax Highlighting # def fibonacci(n): \u0026#34;\u0026#34;\u0026#34;Generate a fibonacci sequence up to n.\u0026#34;\u0026#34;\u0026#34; a, b = 0, 1 while a \u0026lt; n: print(a, end=\u0026#39; \u0026#39;) a, b = b, a + b print() fibonacci(1000) Rust Example # fn main() { let name = \u0026#34;Aditya\u0026#34;; println!(\u0026#34;Hello, {}! Welcome to the technical showcase.\u0026#34;, name); } Inline Code # You can use const x = 10; within a sentence to highlight small snippets.\n"},{"title":"The Architecture of Scalable Systems: A Comprehensive Deep Dive","url":"https://adityabavadekar.github.io/blogs/massive-sample/","date":"2026-05-14","tags":["distributed-systems","architecture","scalability"],"excerpt":" Introduction to Scalability # Scalability is not just a feature; it is a fundamental property of modern software systems. As we move deeper into the era of global-scale applications, understanding how to build systems that grow gracefully with demand is more critical than ever. This post explores the multi-layered complexity of scalability, from low-level resource management to high-level architectural patterns.\nScalability often gets confused with performance. While performance is about how fast a system can process a single request, scalability is about the system\u0026rsquo;s ability to handle an increasing amount of work by adding resources. In a perfectly scalable system, doubling the resources should exactly double the capacity. However, in reality, we often face diminishing returns due to overhead, contention, and coordination.\n"},{"title":"Inspired By","url":"https://adityabavadekar.github.io/inspired/","date":"2026-05-14","tags":[],"excerpt":"This is a curated list of digital spaces and minds that have influenced my thinking or whose technical depth I find inspiring.\nEngineering \u0026amp; Research # Lalit Maganti - Deep dives into systems engineering and performance. Nesbitt - Minimalist technical writing and software design. AI Engineering From Scratch - Fundamental approach to building and understanding AI systems. Stripe Dev: For their exceptional documentation standards and clean, functional aesthetic. I'm always looking for high-signal technical writing. If you have a recommendation, feel free to reach out."},{"title":"Spectral Graph Pruning for Context Optimization in Retrieval-Augmented Generation","url":"https://adityabavadekar.github.io/papers/spectral-graph-pruning/","date":"2026-05-09","tags":["NLP","RAG","Graph Theory","Optimization"],"excerpt":" Abstract # Spectral Graph Pruning (SGP) is a framework for efficient context optimization and compression in Retrieval-Augmented Generation (RAG). It models retrieved text segments as a heterogeneous semantic graph and applies query-biased spectral centrality analysis to identify and retain the most structurally important segments. SGP reduces token consumption by 40-50% while maintaining high reasoning accuracy on multi-hop benchmarks.\nMethodology # The SGP framework constructs a heterogeneous graph representing Chunk Nodes, Entity Nodes, and Structural document hierarchy.\n"},{"title":"Machine Learning: Architectures, Training, and Inference","url":"https://adityabavadekar.github.io/blogs/classical-ml/","date":"2026-04-26","tags":[],"excerpt":" Maths # Linear Algebra: Vectors, Matrices, Eigenvalues, Eigenvectors, Singular Value Decomposition (SVD), \u0026hellip; Calculus: Derivatives, Integrals, Gradient Descent, Chain Rule, \u0026hellip; Probability and Statistics: Distributions, Bayes\u0026rsquo; Theorem, Maximum Likelihood Estimation, Hypothesis Testing, \u0026hellip; Optimization Theory Classical ML # Regression / Classification Clustering Techniques Evalulation: Confusion Matrix, ROC, AUC, Precision, Recall, F1 Score Cross Validation, Train Test Split Bias-Variance Tradeoff, Overfitting vs Underfitting Decision Trees SVMs, KNN, Naive Bayes Clustering: K-Means, Hierarchical Clustering, DBSCAN, \u0026hellip; Dimensionality Reduction: PCA, t-SNE, UMAP, \u0026hellip; Ensemble Methods: Bagging, Boosting, Random Forests, Gradient Boosting, XGBoost, LightGBM, CatBoost Recommendation Systems Probabilistic Models, Logical Models, Geometric Models Explainability: SHAP, LIME, Feature Importance Deep Learning # What is Neuron, Perceptron, Neural Networks Activation Functions: Sigmoid, Tanh, ReLU, Leaky ReLU, Softmax \u0026hellip; Vanishing or Exploding Gradients Layers: Fully Connected, Convolutional, Recurrent, LSTM, GRU, \u0026hellip; Optimization Algorithms (Optimizers): SGD, Momentum, Nesterov, Adagrad, RMSProp, Adam, \u0026hellip; Backpropagation, MLPs, Loss Functions, Regularization, Dropout, Batch Normalization Pytorch Computer Vision: CNNs, ResNets, Object Detection, Image Segmentation Generative Models: GANs, VAEs Natural Language Processing: RNNs, LSTMs, GRUs, Transformers, BERT, GPT, NER Audio and Speech Processing: RNNs, LSTMs, GRUs, Transformers, Wavenet, TTS, ASR Transformers: Attention Mechanism, Self-Attention, Multi-Head Attention, Positional Encoding, BERT, GPT, T5, MoEs, latest advancements Autoencoders Diffusion Models Vision Transformers (ViTs) Multimodal Models Graph Neural Networks (GNNs) Quantization LLMs # Sampling: Temperature, Top-k, Top-p, Beam Search Pretraining, Mid training, Post training, Fine-tuning (Instruction Tuning, RLHF) Fine-tuning: Instruction Tuning, RLHF, LoRA, PEFT, SFT, RAG, QLoRA, adapters Evaluation: Perplexity, BLEU, ROUGE, Human Evaluation, Arena-style evaluations Encoders, Decoders, Encoder-Decoder Architectures Tokenization: WordPiece, Byte-Pair Encoding (BPE), SentencePiece, Unigram Embeddings: Word2Vec, GloVe, FastText, BERT Embeddings, Sentence Embeddings KV Caches Flash Attention Context Length Scaling Sparse Attention New concepts: Mixture of Experts Routing Chain of Thought Tool Use Function Calling Reasoning Models Test-Time Compute Long Context Models Reinforcement Learning # Q-Learning, Policy Gradients, Deep Q-Networks, MDPs, DQNs RL for LLMs: RLHF, RLAIF, DPO Applied AI # LLM APIs Embeddings and Vector Databases RAG Agents (Frameworks: Langchain, Langgraph, Camel, etc.) MCP Inference Optimization Hybrid Search Reranking Workflows MLOps # Experiment Tracking Dataset Versioning CI/CD for ML Monitoring Drift Detection A/B Testing Model Registries Feature Stores Deployment ONNX TensorRT Triton Kubernetes for ML Observability AI Infrastructure # GPUs, TPUs, CUDA Basics Memory Management, Parallelism, Distributed Systems Inference Servers, Serving Architectures Batching, Caching Model Compression Research # Scaling Laws Emergence Mechanistic Interpretability Sparse Autoencoders Model Editing World Models Self-Supervised Learning Contrastive Learning Curriculum Learning Meta Learning "},{"title":"Database Systems","url":"https://adityabavadekar.github.io/blogs/databases/","date":"2026-02-05","tags":[],"excerpt":" Types of databases and how do they work (WIP) # What is a database? # A database is an electronically stored, collection of data. It can contain any type of data, including words, numbers, images, videos, and files.1\nYou can use software called a database management system (DBMS) to store, retrieve, and edit data.1 A database management system (DBMS) is a computerized system that enables users to create and maintain a database.\n"},{"title":"About","url":"https://adityabavadekar.github.io/about/","date":"2026-01-13","tags":[],"excerpt":" Hi there! I\u0026rsquo;m Aditya Bavadekar # About # I am a Full Stack Engineer and AI Researcher specializing in Systems Software, Distributed Infrastructure, and Multi-Agent AI. Specializing in the JavaScript ecosystem (Next.js, React, Node.js), Python, and Rust, I architect scalable solutions that bridge the gap between intelligent systems and secure, low-level infrastructure.\nI like building things, learning how systems work, and sharing what I figure out along the way.\n"},{"title":"How do Payment Systems Work?","url":"https://adityabavadekar.github.io/blogs/payment-systems/","date":"2026-01-13","tags":[],"excerpt":" Payment Systems (WIP) # "},{"title":"Aarogya App","url":"https://adityabavadekar.github.io/projects/aarogya-app/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"Agentd","url":"https://adityabavadekar.github.io/projects/agentd/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"Agentrology","url":"https://adityabavadekar.github.io/projects/agentrology/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"Aragot Assistant","url":"https://adityabavadekar.github.io/projects/aragot-assistant/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"DevNavigator","url":"https://adityabavadekar.github.io/projects/devnavigator/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"HackFinder","url":"https://adityabavadekar.github.io/projects/hackfinder/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"ilockdown","url":"https://adityabavadekar.github.io/projects/ilockdown/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"Limen","url":"https://adityabavadekar.github.io/projects/limen/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"Reconcyl","url":"https://adityabavadekar.github.io/projects/reconcyl/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"RollCall","url":"https://adityabavadekar.github.io/projects/rollcall/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"Search","url":"https://adityabavadekar.github.io/search/","date":"0001-01-01","tags":[],"excerpt":""},{"title":"SSH Public Key","url":"https://adityabavadekar.github.io/ssh/","date":"0001-01-01","tags":[],"excerpt":" adi@archlinux: ~/.ssh $ cat id_rsa.pub ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIF+FkSAln3rVnn2Gamduxq/NV8A1SYojWoAlR3msIKYM some-user@gmail.com Note: Feel free to add my public key to your authorized_keys if you trust me.\n"},{"title":"vrun","url":"https://adityabavadekar.github.io/projects/vrun/","date":"0001-01-01","tags":[],"excerpt":""}]